Article
Computer Science, Information Systems
Mohannad Babli, Jaime A. Rincon, Eva Onaindia, Carlos Carrascosa, Vicente Julian
Summary: The study proposed a deliberation architecture for ambient intelligence healthcare applications, aiming to provide comfort to stressed seniors in assisted living homes. By designing a deliberation function, it achieved context-aware dynamic human-robot interaction, perception, planning capabilities, reactivity, and environment awareness.
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
C. L. Oguego, J. C. Augusto, M. Springett, M. Quinde, C. James-Reynolds
Summary: Preferences play a crucial role in decision-making, and understanding preference management is key to developing systems that guide user choices. By exploring argumentation techniques and implementing them in real-life scenarios, this study validates the effectiveness of argumentation in handling conflicting preferences and inconsistencies. The system implemented in this study not only manages conflicting situations in smart homes, but also interacts with external data sources to provide personalized suggestions to users based on their preferences.
APPLIED ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Jie Wan, MingSong Li, Michael J. OGrady, Xiang Gu, Munassar A. A. H. Alawlaqi, Gregory M. P. OHare
Summary: Real-time activity recognition is essential for smart homes, but is currently dominated by the research community using machine learning and AI techniques. Research mainly relies on pre-segmented data, which may not be sufficient for assistive paradigms dependent on smart technologies.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2021)
Review
Health Care Sciences & Services
Mladjan Jovanovic, Goran Mitrov, Eftim Zdravevski, Petre Lameski, Sara Colantonio, Martin Kampel, Hilda Tellioglu, Francisco Florez-Revuelta
Summary: This study presents a scoping review of AI models in Ambient Assisted Living (AAL), analyzing the specific models used, target domains, technology, and concerns from the end-user perspective. The findings provide insights for the development, deployment, and evaluation of future intelligent AAL systems.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Maria Ahmed Qureshi, Kashif Naseer Qureshi, Gwanggil Jeon, Francesco Piccialli
Summary: This study focuses on how ambient assisted living aids and motivates patients with cardiovascular diseases in self-management, utilizing wearable devices, telemedicine, and mHealth systems. Through a systematic literature review, it explores the integration of new technologies in modern diagnostic systems and the use of IoT sensors, cloud models to enhance medical services.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Chemistry, Analytical
Bruna Maria Vittoria Guerra, Micaela Schmid, Giorgio Beltrami, Stefano Ramat
Summary: Human Action Recognition (HAR) is an evolving field that has impacts on various domains, including Ambient Assisted Living (AAL). This study proposes a monitoring system that detects dangerous situations by classifying human postures using Artificial Intelligence (AI) solutions. The analysis shows that the LSTM approach has better suitability and achieves higher performance compared to the MLP approach.
Article
Computer Science, Theory & Methods
Nancy Gulati, Pankaj Deep Kaur
Summary: This research focuses on developing argument-enabled Social IoT networks, proposing a fuzzy argument-based classification scheme and presenting a framework for fall prevention system for activity recognition in smart home environments.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Automation & Control Systems
Lucia Cascone, Michele Nappi, Fabio Narducci, Ignazio Passero
Summary: The article discusses the development of VPepper, a virtual replica of the Pepper robot, and its interaction with smart objects in a smart home environment. Through the use of digital twin, machine learning procedures can be seamlessly transferred between the virtual replica and the physical robot. The practical application shows promising opportunities for simulation accuracy and machine learning instruments in real settings.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Martin E. Buron Brarda, Luciano H. Tamargo, Alejandro J. Garcia
Summary: In this paper, a decision framework is presented, which allows a group of agents to express their preferences among a set of alternatives using multiple comparison criteria. The authors introduce a special type of argument to support agents' preferences, and demonstrate how the generated arguments are used to obtain the selected alternatives and provide explanations for the choices. They also present a software application that implements their approach and provides interactive tools for obtaining different types of explanations. The authors formally study the argumentation process and prove the consistency of the warranted conclusions supporting the obtained results. They also demonstrate the effectiveness of their framework in choice scenarios with incomplete evidence, and propose optimization techniques for dynamic environments.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Wael Alosaimi, Md Tarique Jamal Ansari, Abdullah Alharbi, Hashem Alyami, Adil Hussain Seh, Abhishek Kumar Pandey, Alka Agrawal, Raees Ahmad Khan
Summary: AAL is an interdisciplinary field aimed at improving the lives of the elderly through technology. AAL systems require high-performance functionality to ensure interoperability, accessibility, security, and consistency. Standardization, continuity, and system development assistance are urgently needed to meet the growing demands.
Article
Computer Science, Information Systems
Usman Mahmood Malik, Muhammad Awais Javed
Summary: This paper proposes an ambient intelligence-assisted computing technique for Industrial IoT, aiming to maximize the number of served tasks and improve computational resource utilization at fog nodes by utilizing contextual information and adaptive computing resource unit sizing.
COMPUTER COMMUNICATIONS
(2022)
Article
Computer Science, Information Systems
Hubert Kenfack Ngankam, Helene Pigot, Sylvain Giroux
Summary: Ambient assisted living (AAL) allows assistance to be provided to older adults based on their context. This often involves transforming their homes into smart homes. Older adults frequently struggle with performing daily activities, especially when cognitive impairments are present. Developing an assistance solution requires a multidisciplinary collaborative approach. This article presents an ontology, OntoDomus, that describes the semantic interactions between AAL, context awareness, smart homes, and the Internet of Things. It focuses on multidisciplinarity and the ambient feedback loop.
Article
Energy & Fuels
Yongdong Chen, Youbo Liu, Junbo Zhao, Gao Qiu, Hang Yin, Zhengbo Li
Summary: Active distribution network faces serious voltage violations due to distributed photovoltaic. Reinforcement learning techniques based on deep learning show superior performance in addressing this issue, but they are typically applied to fixed network topologies and lack learning efficiency. To overcome these challenges, a novel edge intelligence approach is proposed, which includes a multi-agent deep reinforcement learning algorithm with graph attention network and physical-assisted mechanism. This approach captures spatial correlations and topological linkages among nodes and enables agents to adapt to real-time topology variations. It also enhances learning efficiency by employing a physical model for generating reference experiences. The proposed methodology is evaluated on real power distribution systems and outperforms previous approaches in convergence and control performance.
Article
Engineering, Multidisciplinary
Tang Tang, Yue Wang, Li-juan Jia, Jin Hu, Cheng Ma
Summary: The paper discusses the application of the Multi-living agent theory in the design of close-in weapon systems, proposing a uniform quantization framework for livelihood and methods for livelihood adjustment. Analyzing the multi-dimensional operational effectiveness of the missile-gun integrated weapon system and assessing its long-term combat effectiveness against saturation attack. The study also investigates the planning problem of equipment deployment and configuration, introducing objectives of overall livelihood degree and cost-effectiveness with an optimization method based on genetic algorithm.
DEFENCE TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Alessandra De Paola, Salvatore Gaglio, Andrea Giammanco, Giuseppe Lo Re, Marco Morana
Summary: Modern smart environments present challenges in designing intelligent algorithms to assist users, such as trajectory recommendations and itinerary planning in the face of diverse points of interest. A multi-agent itinerary suggestion system is proposed to address these challenges, utilizing reinforcement learning to provide high-quality suggestions and overcome issues like cold-start and preference elicitation. Real-life deployments have shown the effectiveness of the approach in scenarios such as smart campuses and theme parks.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Fernando Terroso-Saenz, Andres Munoz
Summary: The study proposes a predictor for nation-wide mobility, utilizing Graph Neural Network to consider relationships among large geographical regions. The results indicate high accuracy in predicting trip numbers and show that only one model is needed to process all mobility areas.
APPLIED INTELLIGENCE
(2022)
Article
Clinical Neurology
Andre Altmann, Mina Ryten, Martina Di Nunzio, Teresa Ravizza, Daniele Tolomeo, Regina H. Reynolds, Alyma Somani, Marco Bacigaluppi, Valentina Iori, Edoardo Micotti, Rossella Di Sapia, Milica Cerovic, Eleonora Palma, Gabriele Ruffolo, Juan A. Botia, Julie Absil, Saud Alhusaini, Marina K. M. Alvim, Pia Auvinen, Nuria Bargallo, Emanuele Bartolini, Benjamin Bender, Felipe P. G. Bergo, Tauana Bernardes, Andrea Bernasconi, Neda Bernasconi, Boris C. Bernhardt, Karen Blackmon, Barbara Braga, Maria Eugenia Caligiuri, Anna Calvo, Chad Carlson, Sarah J. A. Carr, Gianpiero L. Cavalleri, Fernando Cendes, Jian Chen, Shuai Chen, Andrea Cherubini, Luis Concha, Philippe David, Norman Delanty, Chantal Depondt, Orrin Devinsky, Colin P. Doherty, Martin Domin, Niels K. Focke, Sonya Foley, Wendy Franca, Antonio Gambardella, Renzo Guerrini, Khalid Hamandi, Derrek P. Hibar, Dmitry Isaev, Graeme D. Jackson, Neda Jahanshad, Reetta Kalviainen, Simon S. Keller, Peter Kochunov, Raviteja Kotikalapudi, Magdalena A. Kowalczyk, Ruben Kuzniecky, Patrick Kwan, Angelo Labate, Soenke Langner, Matteo Lenge, Min Liu, Pascal Martin, Mario Mascalchi, Stefano Meletti, Marcia E. Morita-Sherman, Terence J. O'Brien, Jose C. Pariente, Mark P. Richardson, Raul Rodriguez-Cruces, Christian Rummel, Taavi Saavalainen, Mira K. Semmelroch, Mariasavina Severino, Pasquale Striano, Thomas Thesen, Rhys H. Thomas, Manuela Tondelli, Domenico Tortora, Anna Elisabetta Vaudano, Lucy Vivash, Felix Podewils, Jan Wagner, Bernd Weber, Roland Wiest, Clarissa L. Yasuda, Guohao Zhang, Junsong Zhang, Costin Leu, Andreja Avbersek, Maria Thom, Christopher D. Whelan, Paul Thompson, Carrie R. McDonald, Annamaria Vezzani, Sanjay M. Sisodiya
Summary: The study identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness, with differentially expressed genes showing enrichment for microglial markers, particularly activated microglial states. Findings suggest that activated microglia may play a role in cortical thinning in epilepsy.
NEUROPATHOLOGY AND APPLIED NEUROBIOLOGY
(2022)
Article
Computer Science, Information Systems
Fernando Terroso-Saenz, Andres Munoz, Francisco Arcas, Manuel Curado
Summary: A comparison was made between a national-scale Twitter dataset and an official mobility survey in Spain, showing that Twitter could only capture a limited number of mobility features in Spain during the study period.
Article
Computer Science, Interdisciplinary Applications
Fernando Terroso-Saenz, Andres Munoz, Francisco Arcas, Manuel Curado
Summary: In recent years, there has been an increasing interest in using geo-tagged documents from Online Social Networks (OSN) for human-mobility pattern mining, but the reliability of OSN geo-data has not been fully studied. This study compares a nation-scale Twitter (TWT) dataset with an official mobility study to determine the reliability of TWT as a source for human-mobility mining. The results show that TWT can be reliable for human-mobility mining when certain socioeconomic, temporal, and spatial factors are present.
SOCIAL SCIENCE COMPUTER REVIEW
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Andres Munoz, Juan Carlos Augusto, Vincent Tam, Hamid Aghajan
JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS
(2022)
Article
Multidisciplinary Sciences
Siddharth Sethi, David Zhang, Sebastian Guelfi, Zhongbo Chen, Sonia Garcia-Ruiz, Emmanuel O. Olagbaju, Mina Ryten, Harpreet Saini, Juan A. Botia
Summary: This study develops a machine learning-based framework to predict previously unannotated human 3'UTRs. The researchers identify unannotated 3'UTRs associated with genes in various human tissues, with the brain having the highest abundance. These unannotated 3'UTRs are enriched for RNA binding protein (RBP) motifs, particularly in brain-specific cases, and the associated genes play a role in synaptic function.
NATURE COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Pedro Jose Martinez-Garcia, Jorge Mas-Gomez, Jill Wegrzyn, Juan A. Botia
Summary: In this study, a comprehensive bioinformatic analysis was conducted on gene expression data from two different growth stages of grapes. A total of 2170 cis-eQTL were identified in 212 genes modulated at ripening onset. This study provides valuable insights into the genetic variation and complex genetic architecture underlying gene expression during fruit ripening in grapes.
SCIENTIFIC REPORTS
(2022)
Article
Neurosciences
Mary B. Makarious, Hampton L. Leonard, Dan Vitale, Hirotaka Iwaki, Lana Sargent, Anant Dadu, Ivo Violich, Elizabeth Hutchins, David Saffo, Sara Bandres-Ciga, Jonggeol Jeff Kim, Yeajin Song, Melina Maleknia, Matt Bookman, Willy Nojopranoto, Roy H. Campbell, Sayed Hadi Hashemi, Juan A. Botia, John F. Carter, David W. Craig, Kendall Van Keuren-Jensen, Huw R. Morris, John A. Hardy, Cornelis Blauwendraat, Andrew B. Singleton, Faraz Faghri, Mike A. Nalls
Summary: Personalized medicine utilizes machine learning and multimodal data for disease prediction and treatment, showing promising results in the prediction of Parkinson's disease.
NPJ PARKINSONS DISEASE
(2022)
Article
Computer Science, Artificial Intelligence
Fernando Terroso-Saenz, Raul Flores, Andres Munoz
Summary: This paper explores the feasibility and effectiveness of using Twitter data to predict human mobility. By combining Twitter data with government open data sources and using machine learning models for prediction, the results show that Twitter data have considerable value in predicting large-scale human mobility.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Biochemistry & Molecular Biology
Sonia Garcia-Ruiz, Emil K. Gustavsson, David Zhang, Regina H. Reynolds, Zhongbo Chen, Aine Fairbrother-Browne, Ana Luisa Gil-Martinez, Juan A. Botia, Leonardo Collado-Torres, Mina Ryten
Summary: Dysregulation of RNA splicing is implicated in rare and complex diseases. We have developed IntroVerse, a comprehensive resource for exploring intron usage by providing a catalogue of annotated introns and novel junctions. This dataset, generated from extensive RNA sequencing analysis, offers insights into novel transcripts and assessment of splicing noise in introns.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Computer Science, Cybernetics
Fernando Terroso-Saenz, Jesus Soto, Andres Munoz
Summary: Music listening choices can reflect people's emotions, and this study analyzes the most popular songs from 52 countries on Spotify to explore mood trends and contextual factors. A multivariate time series model is proposed to predict preferred music types based on previous listening patterns and contextual factors, showing changes due to the pandemic. The resulting prediction model can forecast music listening preferences in different countries with a low error rate, providing insights for the music and marketing industry.
ENTERTAINMENT COMPUTING
(2023)
Article
Computer Science, Software Engineering
Fernando Terroso-Saenz, Andres Munoz
Summary: This paper proposes a macro open-data mobility study method based on the fusion of cellphone and road traffic sensor data. The results show that models trained with the fusion of these two data sources, especially LSTM and GRU neural networks, provide more reliable predictions compared to models based only on open data sources. It is possible to predict the traffic entering a specific city in the next 30 minutes with an absolute error less than 10%. Thus, this work is a further step towards improving the prediction of human mobility in interurban areas by fusing open data with data from IoT systems.
JOURNAL OF UNIVERSAL COMPUTER SCIENCE
(2023)
Review
Clinical Neurology
Conceicao Bettencourt, Nathan Skene, Sara Bandres-Ciga, Emma M. Anderson, Laura F. Winchester, Isabelle Foote, Jeremy A. Schwartzentruber, Juan Botia, Mike Nalls, Andrew M. Singleton, Brian Schilder, Jack J. Humphrey, Sarah E. Marzi, Christina Toomey, Ahmad L. Al Kleifat, Eric Harshfield, Victoria Garfield, Cynthia Sandor, Samuel Keat, Stefano Tamburin, Carlo Sala Frigerio, Ilianna Lourida, Janice M. Ranson, David Llewellyn
Summary: Genetics and omics studies of Alzheimer's disease and other dementia subtypes provide insights into underlying mechanisms, but there are challenges in enhancing genetic studies, identifying reproducible omics signatures, and utilizing high-dimensional omics data for improved biomarkers. Artificial intelligence and machine learning approaches offer potential solutions, but coordinated multidisciplinary research and diverse phenotyped cohorts are also needed.
ALZHEIMERS & DEMENTIA
(2023)
Article
Clinical Neurology
Amy R. Hicks, Regina H. Reynolds, Benjamin O'Callaghan, Sonia Garcia-Ruiz, Ana Luisa Gil-Martinez, Juan Botia, Helene Plun-Favreau, Mina Ryten
Summary: Genes encoding the non-specific lethal complex are highly correlated with and regulate genes associated with Parkinson's disease. The complex plays a potential role in the regulation of genes and pathways implicated in Parkinson's disease. KANSL1 and KAT8 may serve as useful drug targets for Parkinson's disease.
Article
Computer Science, Information Systems
Fernando Jimenez, Gracia Sanchez, Jose Palma, Luis Miralles-Pechuan, Juan A. Botia
Summary: In machine learning classification problems with high-dimensional datasets, efficient methods are needed to determine the relative importance of attributes and eliminate redundant and irrelevant ones. This paper proposes two new multivariate feature ranking methods based on correlation and consistency, which outperform other methods in cancer gene expression and genotype-tissue expression classification tasks.
